With the volume of vehicles using roadways today, traffic detection and management have become more important. Current intersection control and traffic data collection devices, namely, inductive loops, ultrasonic and radar systems possess limitations in their area coverage for individual devices. Machine vision systems have begun to assist in traffic management. Machine vision systems typically include video cameras overlooking traffic scenes. The video cameras output video images and the machine vision system processes the images to detect, classify and track vehicles passing through the traffic scene. The information derived from the detection, classification and tracking is then used by the machine vision system for intersection control, incident detection, traffic data collection and other traffic management functions.
Machine vision systems analyze a traffic scene by frame-by-frame analysis of video images acquired by video cameras at traffic scenes. The video consists of many video frames taken at constant time intervals, for example 1/30th of a second time intervals. The video is digitized so that the machine vision system analyzes a pixel representation of the scene. A typical digitized video image array for a video frame will contain a 512.times.512 pixel image of the scene. Each pixel has an integer number defining intensity and may have a definition range for three colors of 0-255.
Machine vision systems have advantages over prior traffic detection devices because machine vision systems can directly extract properties of vehicles, such as velocity and acceleration. Prior detection devices, such as inductive loops, inferred these properties based on detection of vehicles at known locations. Besides mere detection of vehicles, some machine vision systems further have the capability to track detected vehicles.
Before a machine vision system can accurately and directly extract traffic properties, such as acceleration and velocity, the machine vision system must be able to map two-dimensional pixel space to three-dimensional real-world measurements. For a machine vision system to have the capability of determining certain vehicle parameters, such as velocity, the system must be able to determine the approximate real-world distance a vehicle has moved and the approximate time the vehicle needed to travel that real-world distance. Machine vision systems, however, evaluate the location and the movement of vehicles within a scene by their location within a video frame. Therefore, the machine vision system must be able to determine the real-world distance a vehicle has traveled from one video frame to the next video frame, based on the location of the vehicle within the video image.
One way to calibrate a machine vision system, in other words, map the pixel space of the video image of a traffic scene to the real-world measurements of the scene, is by physically measuring distances between specific points within regions of interest in a scene. While distances between specific points are measured, these points are contemporaneously located within the video image and the real-world distances between these points are assigned to the corresponding distances between the specific points in the pixel space. This method of calibration is labor and time intensive.
Calibration allows the machine vision system to analyze a pixel representation of a traffic scene and map the real-world measurements to the pixel space. Thus, after calibration, an operator of the machine vision system can ascertain the real-world distances a vehicle has moved while the operator is viewing a display of the video. Further, the machine vision system can determine traffic parameters associated to the vehicle when the vehicle passes through the specific points measured.